The role of Data Analytics has grown in the last years. Thanks to data you better understand your customers and can bring them better services.
In the article “Predictions 2019: Data Analytics Trends to Watch” by Mathias Golombek, we can read an interesting analysis about Data Analytics trends. Here is the link:
Predictions 2019: Data Analytics Trends to Watch
The key topic of the article is the Analytics trends that we can expect during 2019. He made reference that machine learning continues to come of age, and Python is the front-runner. He mentioned the rise of data centricity, data science and business intelligence converge, business intelligence becomes self-service, data skills will continue to be in high demand, more data centers evolve into hyperscale data centers, and public cloud increasingly becomes a three-horse race, distributed ledger technology drives new innovation, and containerization in the hybrid cloud (Golombeck, 2019). Additional trends are Servitization, which means businesses provide service with the product, and Natural Language Processing (NLP) being used in the workplace.
The impact of the trends is strong in businesses since it is necessary to remember that data analytics is becoming more important for all successful companies. Businesses rely on some form of data to help them make decisions for their companies. Now that technology is more advanced, and there is more data available, it requires people with specialized training to analyze the data and make recommendations from it.
My opinion is that data is useful when it is well analyzed and not influenced by biases. The data helps you to know how the departments in your organization are working, how the company, how the marketing efforts, how the customer service and many areas are performing. To collect and review data needs to be a careful and detailed task from all members involved in the analysis efforts.
I believe the trends of using more machine learning can benefit businesses by having more clear understanding of what works and what doesn’t work in marketing. However, I think there is a risk of relying too much on answers provided by calculations. The data analysis can have a positive impact if it is guided by ethics and common sense.
Many marketers are using machine learning to understand, anticipate and act on the problems their sales prospects are trying to solve faster and with more clarity than any competitor. Having the insight to tailor content while qualifying leads for sales to close quickly is being fueled by machine learning-based apps capable of learning what’s most effective for each prospect and customer. Machine learning is taking contextual content, marketing automation including cross-channel marketing campaigns and lead scoring, personalization, and sales forecasting to a new level of accuracy and speed (Columbus, 2018).
This year is almost ending, and most of the trends mentioned by Golombeck are already happening. Since technological advances for managing data continue to expand, we should ask ourselves what the future has in store for us and if we will be prepared to handle it. I think in most cases, we will need to be more educated in data analysis and companies will search for individuals who have experience with it. Also, it necessary to have strong ethics when managing a big amount of information. After all, we can’t forget that behind those numbers, there are real people.
References
Columbus, Louis (2018). 10 Ways Machine Learning Is Revolutionizing Marketing. Forbes. Retrieved from: https://www.forbes.com/sites/louiscolumbus/2018/02/25/10-ways-machine-learning-is-revolutionizing-marketing/#3e0c46655bb6
Golombek, Mathias (2019). Predictions 2019: Data Analytics Trends to Watch. Dataversity. Retrieved from: https://www.dataversity.net/predictions-2019-data-analytics-trends-to-watch/
EK Marketing Blog by Ericka Koehler 